Electronic profile development, storage, use, and systems therefor

ABSTRACT

Examples of the present invention include profiling systems that store, manage, and utilize profile information to take predictive or deterministic action. Embodiments of the invention allow the profiling system to be used as a trusted intermediary where the profile owning entity controls access to their profile information across their network of devices and services.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of pending U.S. patent application Ser.No. 12/334,389, filed Dec. 12, 2008, entitled “ELECTRONIC PROFILEDEVELOPMENT, STORAGE, USE, AND SYSTEMS THEREFOR”, which applicationclaims the benefit of U.S. Provisional Application 61/067,162, filedFeb. 25, 2008, entitled “PLATFORMS, SYSTEMS, AND METHODS FOR DATAHANDLING”. These applications are incorporated herein by reference, intheir entirety, for any purpose.

TECHNICAL FIELD

Embodiments of this invention relate to computing systems and softwarefor the collection, development, analysis, and use of profileinformation.

BACKGROUND

Current systems for providing content items to users vary in theirapproach to user information, for example, in their ability to obtainmeaningful information about their users and the extent to which theirusers control access to and use of their own information.

Some systems simply decide what may be appropriate for or desirable forusers based on a single known data point about the user. For example,location based services receive location information from a user'smobile device and identify nearby businesses, gas stations, or ATMs.Other location-relevant information may be provided as well, such aslocal weather reports. However, the information is selected based onlyon the user's location. The system has no way of knowing if any of theidentified businesses or facts are more relevant for the particular userthan any other.

Some systems guess what may be appropriate or desirable for users basedon a single action. For example, contextual advertising systems mayprovide an advertisement for a web page based in part on a target wordin the web page. These systems have no way of knowing if theadvertisement is actually relevant to the user viewing the web page—theadvertisement is chosen simply because it matches a target word on theweb page. Some systems decide what products may be desirable for a userbased on ratings of other similar products provided by the user. Forexample, some recommendation services receive limited user ratings, orimplicit ratings based on views or purchases, of a certain kind ofproduct—books or movies for example—and recommend other books or moviesthat the user may like based on similarity to items favorably rated,such as authors, themes, actors, directors, genres, and the like.

Location based systems, contextual advertising, and recommendationsystems, are forced to decide what things may be relevant to them on thebasis of the limited known information about a user. These systems maynot achieve a high success rate of delivering information that is trulyrelevant to the user because the recommendations are based on limitedavailable information explicitly shared with the system. The system doesnot know any other information about the user, including informationcollected by or shared with other systems. These systems, however, mayallow the user to have control over their personal information. That is,the user has shared only a limited amount of personal information withthe system.

Other systems may make more intelligent recommendations for users basedon more detailed information about the user, but these systems maysuffer from user privacy problems. For example, deep packet inspectiontechnologies can analyze information sent to and from a user on abroadband network. By inspecting all information sent or received by auser over time, the Internet service provider can develop a clearerpicture of the user and what may be relevant to them. However, thisapproach raises serious privacy concerns because the user may not knowthat their personal information is being collected, and does not controlto whom the information is provided.

These previous systems also suffer from being proprietary to theparticular website or electronic service accessed. For example, websites such as Facebook, Ticketmaster, and ESPN, maintain some profileinformation associated with their users. However, the profileinformation stored by the user at one site is generally inaccessible toothers, depriving the user of its benefit as they travel to otherwebsites. Allowing one site to share information with others againraises privacy concerns. It often may be prohibitive for one system toobtain the necessary user consent to share profile information withanother system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system according to an embodiment ofthe present invention.

FIG. 2 is a schematic illustration of a conceptual database schema foran electronic profile according to an embodiment of the presentinvention.

FIG. 3 is a schematic illustration of a profile management interfaceoperating in a browser window of a display according to an embodiment ofthe present invention.

FIG. 4 is a flowchart illustrating operation of a disambiguation engineaccording to an embodiment of the present invention.

FIG. 5 is a flowchart illustrating operation of an indexing engineaccording to an embodiment of the present invention.

FIG. 6 is a flowchart illustrating operation of a disambiguation engineaccording to an embodiment of the present invention.

FIG. 7 is a flowchart illustrating operation of an analysis engineaccording to an embodiment of the present invention.

FIG. 8 is a schematic illustration of a user interface according to anembodiment of the present invention.

FIG. 9 is a schematic illustration of a user interface according to anembodiment of the present invention.

FIG. 10 is a schematic illustration of a user interface according to anembodiment of the present invention.

DETAILED DESCRIPTION

Certain details are set forth below to provide a sufficientunderstanding of embodiments of the invention. However, it will be clearto one skilled in the art that embodiments of the invention may bepracticed without various of these particular details. In someinstances, well-known computer system components, network architectures,control signals, and software operations have not been shown in detailin order to avoid unnecessarily obscuring the described embodiments ofthe invention.

Embodiments of the invention provide a profiling system that may obtain,expand, manage, store, and use electronic profiles. Electronic profilesdescribed herein include data structures containing information about anentity, all or a portion of which may be used as input to an analysisengine that may take a predictive or deterministic action based in parton the electronic profile. As will be described below, an entity maycontrol the use of all or portions of their electronic profile, allowingit to be used in part or completely to score and select contentresponsive to requests from particular entities. The analysis engineuses information from the electronic profile to take a predictive ordeterministic action, for example, as will be described further below,suggesting products, services, content, organizations, people, or otheritems that may be particularly relevant for the profile owning entity.The entity may be a person or a group of people. The entity may also bea segment of people that share a common attribute. The entity may alsobe a thing such as, but not limited to, a product, place or item ofcontent.

An example of a system 100 according to an embodiment of the presentinvention is shown in FIG. 1. A profiling system 110 includes a profilemanagement system 115, a disambiguation engine 120, and an analysisengine 125. These individual components will be discussed further below.The profiling system 110 generally includes a processor and memory tostore computer readable instructions that may cause the processor toimplement the functionalities of the profile management system 115,disambiguation engine 120, and analysis engine 125 described below. Oneor more of these engines may be implemented on a server or othercomputer or computing device. Although shown as a unitary system, theprofiling system 110 may be implemented as distributed across aplurality of computing devices, with portions of the processingperformed by each of the devices.

A user device 130, which may be implemented as any device with suitableprocessing, memory, and communication capabilities to implement aprofile management interface 135, is in communication with the profilingsystem 110. The user device 130 may accordingly be, but is not limitedto, a personal computer, kiosk, cell phone, personal digital assistant,television set-top box, or music player. The user device 130 may bespecific to a single user, or may be used by multiple users, such as inthe case of a publicly accessible workstation or kiosk. In someembodiments, the user need not be a physical person, but may be arepresentative of a group of people, or may be another automated processor computer program performing a profile entry functionality.Communication between the profiling system 110 and the user device 130may occur through any mechanism. In some embodiments, the profilingsystem 110 may be implemented completely or partially as a web servicethat may communicate with the user device 130 over the Internet usinghttp, in either a secure or unsecured manner, as desired. The profilemanagement interface 135 enables communication with the profilemanagement system 115 to establish, augment, or otherwise manipulateprofile information pertaining to an entity represented by a user usingthe user device 130. The disambiguation engine 120 may receive profileinformation supplied from the user device 130 and further process theinformation to reduce ambiguity in the information provided, as will bedescribed further below. The processing to reduce ambiguity may occurdynamically through interaction with the user device. Any number of userdevices may be in communication with the profiling system 110, includingthe user devices 130 b and 130 c shown in FIG. 1.

Profile information received from the user device 130 and other sourcesis processed by the profile management system 115 and disambiguationengine 120 to generate electronic profiles that are stored in theelectronic profile storage 140. As will be described further below, theelectronic profiles may be database structures and accordingly may bestored in a database as shown in FIG. 1. However, any type of electronicstorage may be used to store electronic profiles and the profiles may bestored in any number of distinct storage locations, and individualprofiles may be distributed across a plurality of storage locations.Electronic profiles will be discussed in greater detail below.

A content provider, or other entity seeking to interact with profileowning entities, may communicate with the profiling system 110 using aprovider device 145. As with the user device 130, any device withsuitable processing, memory, and communication capabilities to implementa content storing interface 150, may be used. Accordingly, the providerdevice 145 may be implemented as, but is not limited to, a servercomputer, personal computer, cell phone, personal digital assistant, orkiosk. The provider device 145 may communicate with the profiling system110 in any manner, wired or wireless.

The provider device 145 is in communication with content storage 155.The content storage 155 is any suitable electronic memory storage thatcontains information the provider may want to share with one or moreprofile owning entities. The content storage 155 may be, but is notlimited to, news or entertainment content, such as text files or mediafiles, databases, advertisements, social contacts, customer relationshipmanagement information, enterprise resource management information,catalog data, inventory, images, movies, charity information, sportsinformation, or combinations of these types of information. Any numberof provider devices may be in communication with the profiling system110 including the provider devices 145 b and 145 c. The additionalprovider devices may have their own associated content storage, or maybe in communication with the content storage 155.

The provider device 145 implements a content scoring interface 150 thatmay be implemented as a processor and a memory storing computer readableinstructions causing the processor to implement the content scoringinterface functionality described. Content scoring will be describedfurther below, but as a general overview, the content scoring interface150 makes some or all of the content in content storage 155 accessibleto the analysis engine 125. The analysis engine 125 may then scorecontent items based on one or more of the electronic profiles stored inelectronic profile storage 140. The output of this process may beprovided to the content scoring interface 150 in a variety of ways,including numerical scores assigned to content in the content storage155 based on its relevance to the electronic profile or profilesconsulted, or a ranked list of content in the content storage 155 listedin ascending or descending relevance order, or an indication of contentitems having a relevance score above or below a threshold relevancescore.

An overview of the use of the system 100 according to an embodiment ofthe invention is now described, although further details are providedbelow. An entity may communicate profile information to the profilingsystem 110 through the profile management interface 135 in communicationwith the profile management system 115. The profile management system115 and the disambiguation engine 120 may refine and expand the profileinformation provided. An electronic profile of the entity is stored inelectronic profile storage 140. While a single electronic profilestorage 140 location is shown in FIG. 1, the electronic profile may insome embodiments be distributed across a plurality of storage locations,including across a plurality of storage locations associated withdifferent physical electronic devices that may be used by an entity.Accordingly, in some embodiments, only a portion of the entity's profilemay be located on the electronic profile storage 140. The entity maythen request information from a provider. In FIG. 1, the user device 130is shown as communicating with the provider device 145. While the entityand the provider may communicate in some embodiments, as shown in FIG.1, using the same devices containing the profile management interface135 and the content scoring interface 150, in other embodiments, theentity and the provider may communicate using different devices. Onreceiving a request for information from an entity, the provider device145 through the content scoring interface 150 requests an analysis fromthe analysis engine 125. The analysis engine 125 accesses the entity'selectronic profile stored in electronic profile storage 140 and,provided the entity has chosen to allow all or a portion of its profileinformation to be used responsive to a request from the provider, scoresthe content in the content storage 155 in accordance with the accessedelectronic profile. The resultant scores are provided to the providerdevice 145 through the content scoring interface 150. Having receivedthe scores, the provider may then communicate content to the entitybased on the scores.

In this manner, the profiling system 110 may serve as a trustedintermediary between an entity and a content provider. The contentprovider receives an analysis of its content based on an entity'sprofile information without actually receiving the profile informationitself. Being able to control the accessibility of the profileinformation, and knowing content providers may not obtain theinformation directly, entities may share a greater amount of informationwith the profiling system 110. Further, through the profile managementsystem 115 and disambiguation engine 120, the electronic profiles may bemore structured while being easily created than those created purelythrough freeform user input. The disambiguation engine 120 may suggestrelated terms for addition to an entity's profile, that the entity mayconfirm or deny.

Having described an overview of an example of a system 100 according tothe present invention, examples of electronic profiles will now bediscussed. Electronic profiles described herein include data structurescontaining information about an entity, all or a portion of which may beused as input to an analysis engine that may take a predictive ordeterministic action based in part on the electronic profile. Forexample, recall electronic profiles may be stored in the electronicprofile storage 140 and used by the analysis engine 125 to identifycontent that may be relevant to the entity associated with theelectronic profile.

Examples of electronic profiles accordingly include data structures. Anytype of data structure may be used that may store the electronic profileinformation described below. In one embodiment, the electronic profileis stored in a relational database. FIG. 2 illustrates a portion of aconceptual database schema 200 for an electronic profile according to anembodiment of the present invention. The database schema 200 isorganized as a star schema, but other organizations may be employed inother embodiments. The schema 200 includes several tables relatingaspects of the electronic profile to one another that provideinformation about the entity owning the electronic profile. The databaseconstructed according to the schema 200 may be stored on generally anysuitable electronic storage medium. In some embodiments, portions of anelectronic profile may be distributed amongst several electronic storagemedia, including among storage media associated with differentelectronic devices used by an entity.

Information stored in an electronic profile about an entity may include,but is not limited to any combination of the following: data,preferences, possessions, social connections, images, permissions,recommendation preferences, location, role and context. These aspects ofan entity may be used in any combination by an analysis engine to takepredictive or deterministic action as generally described above.Examples of aspects of profile information included in the electronicprofile 200 will now be described further.

The electronic profile represented by the schema 200 includes data aboutan entity in a user table 201. While the term ‘user’ is used in FIG. 2to describe tables and other aspects of the profile, the term is notmeant to restrict profiles to individuals or human representatives. Theinformation contained in an electronic profile is generally informationabout an entity associated with the electronic profile, which may alsobe referred to as the entity owning the electronic profile. The entitymay be a person or a group of people. The entity may also be a segmentof people that share a common attribute. The entity may also be a thingsuch as, but not limited to, a product, place, business, or item ofcontent. The entity may be a segment of things that share a commonattribute. The term ‘user’ in FIG. 2 simply refers to the entityassociated with the profile.

Data 202 about the entity stored in the user table 201. The table 201may include a column for each type of data. For example, data associatedwith UserID1 includes name (‘Bob Smith’), address (555 Park Lane), age(35), and gender (Male) of the entity. Data associated with UserID2includes height (5′10″), weight (180), and gender (Female). Dataassociated with UserID2 includes financial information and an address(329 Whistle Way). Data about an entity stored in the user table 201 maygenerally include factual or demographic information such as, but notlimited to, height, address, clothing sizes, contact information,financial information, credit card number, ethnicity, weight, andgender. Any combination of data types may be stored. The user table 201also includes a user ID 203. The user ID may be generated by a systemgenerating or using the electronic profile, or may be associated with oridentical to a user ID already owned by the profile owning entity, suchas an email account or other existing account of the entity. Each entityhaving an electronic profile may have a corresponding user table, suchas the user table 201, stored in the electronic profile storage 140 ofFIG. 1.

Preferences of an entity may also be stored in the entity's electronicprofile. Preferences generally refer to subjective associations betweenthe entity and various words that may represent things, people, orgroups. Each preference of an individual represents that association—“Ilike cats,” for example, may be one preference. Preferences may bestored in any suitable manner. In the schema of FIG. 2, preferences arestored by use of the user preferences table 210, the user preferenceterms table 220, the preference terms table 230, and the preferencequalifiers table 240, which will be described further below. The fourtables used to represent preference in FIG. 2 is exemplary only, andpreferences may be stored in other ways in other embodiments such that aprofile owning entity is associated with their preferences.

Referring again to FIG. 2, the user table 201 of an entity is associatedwith a user preferences table 210. The user preferences table 210includes userIDs 203 of entities having profiles in the electronicprofile storage 140 and lists individual preference IDs 211 associatedwith each userID. For example, the UserID1 is associated withSPORTS-PREFERENCE1 and SPORTS_TRAVEL_PREFERENCE1 in the example shown inFIG. 2. Although shown as including only a few user IDs 203, the userpreferences table 210 may generally include a list of multiple user IDsknown to the profiling system and a list of individual preference IDsassociated with the userIDs. In this manner, an entity's preferences maybe associated with the data related to the entity. Generally, any stringmay be used to represent a preference ID. Also included in the userpreference table 210 are qualifier IDs 212 that are used to record anassociation with terms contained in the preference. The qualifiers willbe discussed further below.

Each preference ID has an associated entry in a user preference termstable 220. The user preference terms table 220 contains a list of termIDs associated with each user preference ID. In FIG. 2, for example, thepreference ID SPORTS_PREFERENCE1 is shown associated with TermID1 andTermID2. Any string may generally be used to represent the term IDs.Each TermID in turn is associated with an entry in a preference termtable 230. The preference term table 230 lists the actual termsrepresented by the TermID. A term may generally be any string and isgenerally a unit of meaning, which may be one or more words, or otherrepresentation. As shown in FIG. 2, the preference terms table 230indicates the TermID1 is associated with the term Major League Baseball.Although only one term is shown associated with the TermID1, any numberof terms may be so associated.

Accordingly, as described above, an entity may be associated withpreferences that ultimately contain one or more terms. However, therelationship between the entity and the terms has not yet beendescribed. An entity's preferences may include a scale of likes,dislikes, or both of the entity. Further an entity's preferences mayinclude information about what the entity is or is not, does or does notdo in certain circumstances. In the schema 200 of FIG. 2, eachpreference may be associated with one or more qualifiers, as indicatedby an association between the preference ID and a qualifier ID in theuser preferences table 210. A term associated with each qualifier ID isthen stored in a preference qualifiers table 240. Qualifiers describethe relationship of the preference terms to the profile owning entity.Examples of qualifiers include ‘like’ and ‘dislike’ to describe apositive or negative association with a preference, respectively. Otherqualifiers may be used including ‘when’, ‘when not’, ‘never’, ‘always’,‘does’, ‘does not’, ‘is’, and ‘is not’ to make more complex associationsbetween preference words and the profile owning entity. As shown in FIG.2, the qualifier QualID1 represents the association ‘like’ and, QualID2represents the association ‘dislike’.

Accordingly, the structure shown in FIG. 2 encodes two preferences foran entity represented by UserID1. SPORTS_PREFERENCE1 indicates UserID1likes Major League Baseball and the Seattle Mariners. SPORTS_PREFERENCE2indicates UserID1 likes Fenway Park. Similarly, UserID2 hasSPORTS_PREFERENCE2, which indicates UserID2 dislikes Major LeagueBaseball and the New York Yankees. UserID3 has SPORTS_PREFERENCE3, whichindicates UserID3 likes Derek Jeter.

The manner of storing preferences using the tables described in FIG. 2may aid in efficient storage and analysis by allowing, for example,multiple termIDs to be associated with multiple user preference IDswithout requiring storing the individual terms multiple times in theprofile storage 140 of FIG. 1. Instead, multiple associations may bemade between the termID and multiple user preferences. However, asdiscussed, generally, any data structure may be used to encode anelectronic profile of an entity. In some embodiments, a profile may berepresented and optionally stored as a vector or index. The vector mayuniquely identify an entity associated with the profile. For example,the profile vector may represent a plurality of axes, each axisrepresenting a term, word, or user device, and the vector include bitsassociated with each term, word, and user device to be included in theprofile.

Further information regarding an entity may be stored in an entity'selectronic profile including possessions, images, social connections,permissions, recommendation preferences, location, roles, and context.Although not shown in FIG. 2, these further aspects may be stored asadditional star tables associated with the central user table 201.Possessions of the entity may include things the entity owns or hasaccess to including, but not limited to, gaming systems, cell phones,computers, cars, clothes, bank or other accounts, subscriptions, andcable or other service providers.

Social connections of the entity may include, but are not limited to,connections to friends, family, neighbors, co-workers, organizations,membership programs, information about the entity's participation insocial networks such as Facebook, Myspace, or LinkedIn, or businesses anentity is affiliated with.

Permissions for accessing all or a portion of the electronic profile aredescribed further below but may include an indication of when anentity's profile information may be used. For example, an entity mayauthorize their profile information to be used by the profiling systemresponsive only to requests from certain entities, and not responsive torequests from other entities. The permissions may specify when, how, howoften, or where the profiling system may access the entity's profileresponsive to a request from a specific entity, or type of entity. Forexample, an entity may specify that sports websites may obtaininformation about content relevant to the entity's profile, but thatbanks may not. As generally described above, only the profiling systemhas direct access to the stored profile information, and the profileinformation is not generally shared with content providers that mayrequest scoring of their content based on the entity's profile. However,the scoring may only be undertaken in some embodiments when the entityhas granted permission for their profile to be used to provideinformation to the particular content provider.

Recommendation preferences may include whether the entity would like oraccept recommendations for additional information to be added to theirelectronic profile, or for data or possessions. The recommendationpreferences may specify which entities may make recommendations for theelectronic profile owning entity and under what conditions.

Location information of the entity may include a current locationdetermined in a variety of levels of granularity such as, but notlimited to, GPS coordinate, country, state, city, region, store name,church, hotel, restaurant, airport, other venue, street address, orvirtual location. In some embodiments location information may beobtained by analyzing an IP address associated with an entity.

Roles of the entity may include categorizations of the entity'srelationships to others or things including, but not limited to, father,mother, daughter, son, friend, worker, brother, sister, sports fan,movie fan, wholesaler, distributor, retailer, and virtual persona (suchas in a gaming environment or other site).

Context of the entity may include an indication of activities or modesof operation of the entity, including what the entity is doing in thepast, present, or future, such as shopping, searching, working, driving,or processes the entity is engaged in such as purchasing a vacation.

As will be described further below, all or a portion of the electronicprofile may be used as an input to an analysis engine. In someembodiments, there may be insufficient data about an individual to havea meaningful output of the analysis engine based on their electronicprofile. Accordingly, in some embodiments the profile of a segmentsharing one or more common attributes with the individual may be used asinput to the analysis engine instead of or in addition to theindividual's profile. The profile of a segment may also be used toselect content that may be relevant for that segment of entities, andpass content to entities that share one or more attributes with thesegment.

Having described exemplary mechanisms for storing profile informationand the content of electronic profiles, exemplary methods and systemsfor obtaining profile information will now be discussed. Profileinformation may generally be obtained from any source, including from arepresentative of the profile owning entity, other individuals, or fromcollecting data about the profile owning entity as they interact withother electronic systems. In some embodiments, referring back to FIG. 1,profile information may be directly entered by a profile owning entityor their representative from the user device 130 using the profilemanagement interface 135.

The profile management interface 135 may take any form suitable forreceiving profile information from a profile owning entity or theirrepresentative. In one embodiment, the profile management interface 135includes an application operating on the user device 130. Theapplication on the user device 130 may communicate with the profilingsystem 110. In one embodiment, the disambiguation engine, analysisengine, or both may be implemented as an application programminginterface (API), and the application operating on the user device 130may call one or more APIs operated by the profiling system 110. In someembodiments, the application on the user device 130 that is incommunication with the profiling system 110 operates in an Internetbrowser window, and one embodiment of the profile management interface135 is shown in FIG. 3 operating in a browser window of a display 305 ofthe user device. A profile owning entity, or a representative of thatentity, may enter profile information into the preference entry field310. Prior to entering information, the entity may have identifiedthemselves to the profiling system by, for example, entering a username,password, or both, or other methods of authentication may be usedincluding identification of one or more user devices and their contextassociated with the entity. When entering profile information into thepreference entry field 310, the entity may also select a qualifierassociated with the profile information using a qualifier selector 308.The qualifier selector 308, which may be unique for the entity in someembodiments, may include a drop-down menu, buttons depicting differentqualifiers, or other mechanisms. For example, the qualifier selector 308may include a button for ‘Like’ and one for ‘Dislike’ so an entity couldspecify that they like or dislike the terms they provide in thepreference entry field 310. The entity may submit the entered profileinformation to the profile management system 115 of the profiling system110 in FIG. 1. Information may be submitted, for example, by pressing anenter key, or clicking on an enter button displayed in the browserwindow 302. The information may be communicated to the profilemanagement system 115 using any suitable communication protocol,including http.

Accordingly, profile owning entities may provide profile information tothe profile management system 115. The profile information may bedirectly captured—“I like cats” in the case of a preference, or “I am afather” in the case of a role. However, in some instances, the providedprofile information may be ambiguous, such as “I like the giants.” Itmay be unclear whether the profile owning entity intends to indicate apreference for the New York Giants, the San Francisco Giants, or largepeople.

The profile information submitted by an entity may accordingly besubmitted to the disambiguation engine 120 of FIG. 1. As will bedescribed further below, the disambiguation engine 120 may provide alist of relevant terms that may be displayed in the disambiguationselection area 320 of FIG. 3. An entity may then select the relevantterms from the disambiguation list for addition to the profile beingmanaged. Alternatively or in addition, an entity may select or otherwiseindicate, such as by right-clicking, one or more terms displayedanywhere in the browser window, or more generally displayed by the userdevice, that a term should be added to the entity's profile.Alternatively or in addition, embodiments of a profiling system mayidentify an action of the entity and automatically add a related term tothe electronic profile of the entity. After processing by the analysisengine 125, which will be described further below, relevant content maybe displayed in the content area 330. In some embodiments, the contentarea 330 may not be provided on a same screen with the profilemanagement interface 135, and in some embodiments the content area 330need not be on the same user device. That is, while profile informationmay be entered or revised on one device, content displayed or providedbased on that profile information may be provided on a different devicein some embodiments.

Accordingly, the disambiguation engine 120 functions to select terms,based on preference information input by an entity, that may also berelevant to the entity and may be considered for addition to theentity's electronic profile. In one embodiment, the disambiguationengine 120 may simply provide a list of all known terms containing theentity's input. For example, if the entity entered “giants,” adictionary or sports listing of all phrases or teams containing the word“giants” may be provided. While this methodology may accurately captureadditional profile information, it may be cumbersome to implement on alarger scale.

Accordingly, the disambiguation engine 120 may function along with anindexing engine 420 as shown in FIG. 4. Generally, the indexing engine420 accesses one or more content sources 410 to analyze the contentstored in the accessed content sources 410 and generate an indexedcontent store 430. Although shown as separate storage, the indexedcontent store 430 may include indexing information stored along with thecontent from the content sources 410, or may include only index recordsrelated to the content in the content sources 410. The index informationgenerally includes information about the relative frequency of terms inthe content from the content sources 410. In this manner, as will bedescribed further below, terms may be identified that frequently appearalong with a query term, or in a same pattern as a query table. Thedisambiguation engine 120 may then access the indexed content store 430to more efficiently identify terms related to preferences expressed byan entity. The expressed preference may be stored in one storagelocation, or distributed across multiple storage locations.

The indexing engine 420 may generally use any methodology to indexdocuments from the content sources 410. The indexing engine 420generally includes a processor and memory encoded with computer readableinstructions causing the processor to implement one or more of thefunctionalities described. The processor and memory may in someembodiments be shared with those used to implement the disambiguationengine, analysis engine, or combinations thereof. In one embodiment, avector space representation of documents from the content sources 410may be generated by the indexing engine 420. A vector representation ofeach document may be generated containing elements representing eachterm in the group of terms represented by all documents in the contentsources 410 used. The vector may include a term frequency—inversedocument frequency measurement for the term. An example of a method thatmay be executed by the indexing engine 420 is shown in FIG. 5. FIG. 5further demonstrates an example in which an indexed content store 430may be created specific to a particular category. In some embodiments,however, the indexed content store may be generalized to one or morecategories. However, in embodiments where the indexed content store 430is specific to a single category of information, it may be advantageousto provide several content stores (which may be physically stored in thesame or different media), each containing indexed content for a specificcategory. In this manner, the indexing performed by the indexing engine420 will be specific to the category of information, and may in somecases enable greater relevance matching than querying a general contentstore.

Proceeding with reference to FIG. 5, the indexing engine may receive alist of category specific expert content 512. The expert content may,for example, include a group of content in a particular category thatmay be considered representative of content in the category (using, forexample, the Wikipedia Commons data set, or any other collection ofinformation regarding a particular category). The indexing enginelocates the category specific content in the list over the Internet orother digital source of category-specific content 510. The source ofcategory specific content 510 may be located in a single storage medium,or distributed among several storage mediums accessible to the indexingengine over the Internet or other communication mechanisms.

The indexing engine extracts the text 514 from the expert content andmay perform a variety of filtering procedures such as wordnormalization, dictionary look-up and common English term removal 516.During word normalization, tenses or variations of the same word aregrouped together. During dictionary look-up, meanings of words can beextracted. During common English term removal, common words such as‘and’ or ‘the’ may be removed and not further processed. Grammar,sentence structure, paragraph structure, and punctuation may also bediscarded. The indexing engine may then perform vector spaceword-frequency decomposition 518 of the extracted text from eachdocument. The use of the term document herein is not meant to limit theprocessing of actual text documents. Rather, the term document refers toeach content unit accessed by the indexing engine, such as a computerfile, and may have generally any length.

During the decomposition, each document may be rated based on the termfrequency (TF) of the document. The term frequency describes theproportion of terms in the document that are unique. The term frequencymay be calculated by the number of times the term appears in thedocument divided by the number of unique terms in the document. A vectorof term frequencies may be generated by the indexing engine to describeeach document, the vector having elements representing a term frequencyfor each term contained in the entire content store analyzed.

The vector representing each document may also contain an inversedocument frequency (IDF) measure, that reflects how often the term isused across all documents in the content store, and therefore a measureof how distinctive the term may be to specific documents. The IDF may becalculated as the log of the number of documents containing the termdivided by the number of documents in the content store.

Accordingly, a term frequency-inverse document frequency (“TF-IDF”)score for a term and a document within may be determined by multiplyingthe term frequency value for the term in the document and the inversedocument frequency score for the term in the content store. In thismanner, terms having a high TF-IDF score may be more representative ofthe document, content store, or both, than those having a low TF-IDFscore (which may be ubiquitous terms throughout the content store suchas the term ‘the’ or ‘and’).

In some embodiments, a Kullback-Leibler Divergence, D_(KL) may also beincluded in a vector representation of a document. D_(KL) may provide ameasure of how close a document is to a query—generally, how much commoninformation there is between the query and the document. D_(KL) is ameasure of a distance between two different probabilitydistributions—one representing the distribution of query terms, and theother representing the distribution of terms in the document. D_(KL) maybe calculated as:

${D_{KL}\left( p||q \right)} = {\sum\limits_{i}{p_{i}{\log \left( \frac{p_{i}}{q_{i}} \right)}}}$

where p is the distribution of terms in the document, q is thedistribution of query terms, and i represents each term. Thedistribution of terms in the document may be a vector with entries foreach term in a content store, where the entries are weighted accordingto the frequency of each term in the document. The distribution of queryterms may be a vector with entries for each term in a content store,where the entries are weighted according to the frequency of each termin the query.

Accordingly, using TF-IDF, Kullback-Leibler Divergence, other methods ofdocument relevance measurements, or combinations thereof, the indexedcontent store 430 of FIG. 5 contains one or more content indexesrepresenting a measure of the importance of various terms to eachanalyzed document.

Having described the indexing of documents, a process for disambiguatinga preference by the disambiguation engine 120 using the indexed contentstore 430 is illustrated in FIG. 6. An entity declares 610 a preference,for example by entry into the preference entry field 310 of FIG. 3. Thedisambiguation engine 120 then selects an expert content store 612 toquery using the declared preference. The selection may be made in avariety of ways. In some embodiments, a single content store is used andno selection need be made. In other embodiments, the disambiguationengine 120 receives contextual information about the entity enteringpreference information, and the contextual information is used to selectthe expert content store. For example, in one embodiment, thedisambiguation engine receives information that the entity enteringprofile information is doing so from a sports-related website, andaccordingly, an expert sports content store may be selected.

Documents in the expert content store are rated 614, as described above,based on their relevance to individual terms. In some embodiments, therating is conducted once the preference is entered, while in others, thealready stored vectors containing the measurements are accessed. A setof most relevant documents to the expressed preference may beidentified. The most relevant documents may be identified by calculatinga relevance number for each document based on the preference terms. Arelevance number represents the relevancy of each document to thepreference, using the entered preference terms. Embodiments of therelevance number use a 0-100 scale, and may accommodate a multi-termpreference. In other embodiments, other scales or ranges may be used forthe relevance number including 0-10, or negative numbers may be used.Negative relative numbers may be used in some embodiments to expressscores relative to profile aspects an entity has provided a negativegrammar for, such as ‘dislike’ or ‘not’. The relevance number for asingle term may generally be calculated as a normalized TF.IDF value. Inone embodiment, the calculation may be made by subtracting a minimumTF.IDF value for all terms in the indexed content store from the TF.IDFvalue of the term and dividing the result by the difference between themaximum TF.IDF value for all terms in the indexed content store in theminimum TF.IDF value for all terms in the indexed content store. Formultiple terms in a preference, the relevance number of each documentmay be given as:

${RelevanceNumber} = {\frac{1}{NTerms}{\sum\limits_{i = 1}^{NTerms}\frac{{{TF}.{IDF}_{i}} - {\min \left( {{TF}.{IDF}} \right)}}{\left( {{\max \left( {{TF}.{IDF}} \right)} - {\min \left( {{TF}.{IDF}} \right)}} \right)}}}$

NTerms is the number of terms in the query. The relevance numberaccordingly is a sum of the relevance numbers for each term in thequery, divided by the number of terms. In this manner, the relevancenumber represents a normalization of term-by-term relevance scores forindividual terms. In this manner, the relevance number is based in parton the TF-IDF value for that term, but may be normalized with themaximum and minimum TF-IDF values for that term across all documents ina content store or other set. The relevance number calculated as aboveis accordingly a number between 0 and 100. The Kullback-LeiblerDivergence, D_(KL), may also be used as a relevance number to scorecontent items from a content store, or across multiple content stores.In the case of D_(KL), a lower D_(KL) number indicates a more relevantcontent item (as it may indicate the information space between the itemand the preference is small).

While in some embodiments, the calculation of relevance numbers may notchange over time as the profiling system operates, in some embodimentsrelevance numbers or the method for calculating relevance numbers, maybe modified in a variety of ways as the profiling system operates. Therelevance numbers may be modified through entity feedback or otherlearning methodologies including neural networks. For example, relevancenumbers as calculated above may be used to develop a set of neuralnetwork weights that may be used to initialize a neural network that mayrefine and learn techniques for generating or modifying relevancevalues. The neural network may be trained on a set of training cases,that may be developed in any of a variety of ways, including by usingentity selection of a document to set a target value of a resultantrelevance number. During training, or during operation of the profilingsystem, error functions may be generated between a desired outcome (suchas a training case where an entity or administrator specifies therelevance score, or a situation in operation where entity feedbackindicates a particular relevance score) and a calculated relevancenumber. The error function may be used to modify the neural network orother system or method used to calculate the relevance number. In thismanner, the computation of relevance numbers, and in some embodiments,the relevance numbers themselves, may change as the profiling systeminteracts with content items and entities. For example, a relevancevalue for a content item may be increased if entity feedback indicatesthe content item is of greater or lesser relevance. The entity feedbackmay be explicit, such as indicating a degree of relevance the entitywould assign to the content item, or implicit, such as by identifyingmultiple entities have selected the content item or responded to thecontent item to a degree that indicates the relevance number should behigher, or lower, than that assigned by the profiling system. Entityfeedback may also include feedback obtained by monitoring the activity,selections, or both of one or more entities without necessarilyreceiving intentional feedback from the entity. Examples of neuralnetworks, entity feedback modification, and other computer learningtechniques usable with embodiments of the present invention aredescribed in co-pending U.S. Provisional Application 61/122,282,entitled “Determining relevant information for domains of interest,”filed Dec. 12, 2008, which application is hereby incorporated byreference in its entirety for any purpose.

Referring back to FIG. 6, the set of significantly relevant documentsmay be identified by setting a threshold relevance number, or by settinga fixed number of results, and selecting that number of results inrelevance number order, regardless of the absolute value of therelevance number. In some embodiments, the most relevant documents areselected by identifying a place in a relevance-ranked list of documentswhere a significant change in relevance score occurs between consecutiveresults. So, if, for example, there are documents with relevance numbersof 90, 89, 87, 85, 82, 80, 60, 59, 58 . . . then a threshold relevancenumber of 80 may be selected because it occurs prior to the relativelylarger twenty-point relevance drop to the next document.

After the most relevant documents have been selected, the disambiguationengine may determine the most distinctive related key words 616 in thosedocuments. The most relevant keywords may be determined by weighting thehighest TF.IDF terms in the documents by the relevance number of thedocument in which they appear, and taking a sum of that product over allthe documents for each term. The terms having results over a threshold,or a fixed number of highest resulting terms, may be selected by thedisambiguation engine as most distinctive related keywords 616. Theseselected keywords may be presented to the entity to determine if thekeyword is useful 620. For example, the keywords may be listed in thedisambiguation selection area 320 of FIG. 3. The preference enteringentity may find that one or more of the identified keywords helps torefine the preference they have entered, or for other reasons should beincluded in their electronic profile, and may indicate the keywordshould be added 622 to their preference. The disambiguation engine mayfurther continue the disambiguation operation by repeating the processshown in FIG. 6 using the added preference terms. If keywords are notidentified as belonging to an entity's preference, the declaredpreference is stored 624.

Examples of systems and methods for identifying relevant terms andindexing that may be used to implement disambiguation and indexingengines in accordance with embodiments of the present invention aredescribed in co-pending U.S. Provisional Application 61/122,282entitled, “Determining relevant information for domains of interest,”filed Dec. 12, 2008, which application is hereby incorporated byreference in its entirety for any purpose.

Accordingly, examples of the entry of profile information and refinementof entered profile information have been described above that mayfacilitate the creation and storage of electronic profiles. Referringback to FIG. 1, the information contained in an entity's electronicprofile may be used by the analysis engine 125 to take a predictive ordeterministic action. A variety of predictive or deterministic actionsmay be taken by the analysis engine 125 based in part on informationcontained in an entity's electronic profile. Products, things,locations, or services may be selected and suggested, described, orpresented to an entity based on information contained in the entity'selectronic profile. In other embodiments, other entities may be notifiedof a possible connection to or interest in an entity based on theirelectronic profile. Content on a website browsed by an entity may bemodified in accordance with their profile in some embodiments. Thecontent modification may include the ordering or ranking of content inthe display, highlighting of content, shaping of content, orcombinations thereof. The profiling system 110 may also generate orassist in the provider device generating a notification, alert, email,message, or other correspondence for the entity based on its profile.Accordingly, the analysis engine may take action for the entity or forthird parties based on the entity's profile information. In oneembodiment, which will be described further below, the analysis engine125 selects content for presentation to the entity based on theirelectronic profile.

An example of operation of the analysis engine 125 to select relevantcontent for an entity is shown in FIG. 7. The analysis engine 125accesses 710 one or more aspects, such as a preference, in an entity'selectronic profile. In some embodiments, a single stored preference isaccessed, in some embodiments selected preferences may be accessed, andin some embodiments all stored preferences may be accessed. In someembodiments, other aspects of the profile may be accessed instead of orin addition to one or more preferences. The analysis engine 125 mayaccess an entity's electronic profile responsive to a request from theentity or a third party, such as the provider device 145 in FIG. 1, toprovide relevant information for the entity. The selection of whichpreferences associated with an entity to access may in some embodimentsbe made according to the context of the request for analysis. Forexample, if the request comes from a sports content provider, one ormore sports-related preferences may be accessed. In other embodiments,multiple preferences may be accessed and the context of the request orof the entity may alter the manner in which the relevance number iscomputed. For example, in some embodiments a total relevance number iscalculated by summing individual relevance numbers calculated using arespective preference. A weighted sum may also be taken, with the weightaccorded to each individual relevance number based on the preferencewith which it is associated. Accordingly, an entity's context, which maybe stored in the entity's electronic profile, may determine theweighting of individual preferences in calculating a relevance number.

In addition to selecting relevant content for a user based on theirelectronic profile, in some embodiments the analysis engine 125 mayalternatively or in addition select entities having profiles, orportions of profiles, most relevant to a particular set of content. Forexample, referring back to FIG. 1, the provider device 145 maycommunicate an indication of selected content, or all content, from thecontent storage 155. The analysis engine 125 may then score one or moreelectronic profiles 140 (or aspects of those profiles) based on thecontent, as generally outlined above. The analysis engine 125 may thenreport to the provider device 145 a selection of entity profiles thatmay be relevant to the content provided by the provider device, orreport back an aspect of profiles that are relevant. For example, theanalysis engine may indicate particular entities that are relevant tothe content, or an aspect of those entities—such as reporting thatentities who like horses or sports appear to be relevant to the contentprovided by the provider device 145. This may aid the provider intargeting their content more effectively or preparing mailings or othercommunications to users. As above, in some embodiments, electronicprofiles are only utilized when the profile specifies it may be used toconduct analysis for the provider.

In other embodiments, a specific request may not be required to beginthe process shown in FIG. 7. The analysis engine 125 may select 712 oneor more content indices for analysis based on a context in which theanalysis 125 is operating. In some embodiments, the content index orindices to use may already be known, or there may only be one, in whichcase the selection 712 may not be necessary. In other embodiments, thecontext surrounding the request for analysis may allow the analysisengine 125 to select one or more content indices for analysis. Forexample, if the provider device 145 of FIG. 1 is a sports online serviceprovider, the analysis engine 125 may select a sports related contentindex. Or if the provider, such as the provider device 145, requestsanalysis of a specific content store, such as the content in storage 155of FIG. 1, the analysis engine may select an index associated with thecontent in storage 155.

Referring back to FIG. 7, the analysis engine scores 714 content in theselected indices based on the accessed preferences. The scoring processmay occur in any manner, including a manner that allows the analysisengine to evaluate content items based on terms in the storedpreference. In one embodiment, the scoring process includes assigning arelevance number to content items based on the preference as describedabove with reference to FIG. 6 and the document rating 614 performedduring preference disambiguation. However, in this case, the contentitems are simply scored and further analysis of relevant terms withinthe document may not be done, as was done during preferencedisambiguation. Examples and techniques for content scoring usable withembodiments of the present application are described in co-pending U.S.Provisional Application 61/122,282 entitled “Determining relevantinformation for domains of interest,” filed Dec. 12, 2008, whichapplication is incorporated herein by reference in its entirety for anypurpose.

Accordingly, content items in the selected indices may be scored bycalculating a relevance number using the term(s) in the accessedelectronic profile preference. Relevant content may then be selected 716in a similar manner to the selection of documents and terms for thedisambiguation of preferences described above. That is, content may beselected having a relevance number over a threshold, or a fixed numberof highest rated content items may be selected, or all content itemspreceding a sharp decline in relevance number may be selected. Theselected content items, their ratings, or both may then be transmittedto the provider device 145 of FIG. 1 or, in some embodiments, directlyto the user device 130. The selected content items may be displayed inthe content area 330 of the user device display shown in FIG. 3. Theprovider device, user device, or both, may handle received content inaccordance with its relevance number and may, for example, display thecontent differently or at a different time based on its relevance. Insome embodiments, accordingly, the relevance number need not be used toselect content, but may be used to change the way one or more contentitems are handled by the user device or provider device.

Selected content items may be displayed using other interfaces, and maybe arranged according to their relevance number. For example, contentitems identified by the analysis engine may be displayed to the entityin order of increasing or decreasing relevance. An embodiment of a userinterface 800 to display relevant content to a user is shown in FIG. 8.The user interface 800 is displayed on a touch screen display 840 of theuser device 130, shown in FIG. 8 implemented as a handheld device. Theuser interface 800 displays a plurality of content items 810-838. Eachitem may include a picture, text, a link to further content representedby the picture or text, or combinations thereof. The content items arepositioned within the user interface 800 according to their relevance tothe entity's profile. The user interface contains content itemspositioned to appear as though they were laid on a surface of a sphere,although the display 840 may be flat. In one embodiment, the contentitems are arranged such that they are placed a distance from a center ofthe user interface 800 that corresponds to their relevance number. Thatis, content items may have decreasing relevance according to theirangular distance from a center of the user interface 800.

Accordingly, the center content item 810 may be a content item having ahighest identified relevance number, while content items 820, 816, 830,and 812 have next highest relevance numbers, content items 836, 826,822, and 832 have the next highest relevance numbers, and content items834, 824, 828, and 838 have the next highest. The user interface 800 maybe interactive, such that a user may select a line of content itemseither in longitude or latitude by, for example, touching the line. Onceselected, the user may scroll across latitude or longitude. Furtherrelevant content (not shown in FIG. 8) may accordingly be rotated intoview.

The user interface 800 may also be used to disambiguate preferenceinformation, as generally described above. A user entered preferenceterm may appear as the item 810. Related keywords identified by thedisambiguation engine 120 according to an embodiment of the processshown in FIG. 6 may be displayed in the locations shown by items812-838, again generally placed at an angular distance determined bytheir relevance to the item 810. In some embodiments, successive bandsof displayed content or disambiguation terms refine the relevance indifferent ways. For example, in one embodiment, the item 810 correspondsto an entity expressed preference term. Related keywords identifiedaccording to the term 810 may be placed in the latitude band of 810,such as 820, 830, and others which may not be shown. The next latitudeband including items 832, 812, and 822, may be generated by identifyingrelevant keywords using two terms. For example, a relevant keyword maybe displayed as item 832 using both the term at item 810 and at item 830to query a content store.

Similarly or in addition, different latitude or longitude bands maycorrespond to different categories of information. For example, if anentity expressed preference term is ‘baseball’, baseball may be shown atitem 810. Different names of baseball players may accordingly bedisplayed at items 812, 814, 816, and 818 while different names ofbaseball teams may be displayed at items 822, 824, 826, and 828. Asdescribed above, the term shown at item 822 may be determined byrelevance to multiple terms including the term at positions 810 and 812.Accordingly, if the player “Alex Rodriguez” appears at item 812, theteam that player plays for (the Yankees) may appear at item 822. Asdescribed above, a user may then select any latitude or longitude bandand rotate it to bring additional options into the display area. If theentity viewing the user interface 800 determines any of the displayedpreference disambiguation terms to be relevant to their preference, theymay so indicate, by for example, touching or tapping the relevant item,and it may be added to the entity's preference as described above withreference to FIG. 6. In other embodiments, the user interface 800 mayinclude items displayed as though arranged on a plurality of nestedspheres. The different spheres may contain different categories ofrelevant keywords. So, for example, the names of baseball players may bedisplayed at all item positions shown in FIG. 8 while anothersphere—larger or smaller in radius than the illustrated sphere—displaysthe names of baseball teams.

Another embodiment of a user profile management interface is shown inFIGS. 9 and 10. Again, the embodiment is shown implemented on the userdevice 130, shown as a hand held device having the touch screen display840. In a first view, the user device 130 displays a preference entryfield 910 and may also display terms associated with two storedpreferences 920 and 930. The stored preference 920 includes terms Babe,ruth, grand jury, barry, magowan, bonds, and record. The storedpreference 930 includes terms Seattle Mariners, Seattle, and Mariners.An entity may enter terms for a new preference by entering terms intothe preference entry field 910. As an example, an entity may enter theterm ‘Dodgers’ into the preference entry field 910. A clarification viewmay then be displayed by the user device 130, as shown in FIG. 10. Theclarification view includes terms 1010 identified by the disambiguationengine as possibly being relevant to the expressed term. Accordingly,the terms run, score, pitching, hit, Los Angeles, los, and angeles, aredisplayed by the user device 130 in FIG. 10. An entity may select any ofthese terms as relating to their profile, and transmit them to theprofiling system for inclusion in the entity's profile. For example, theentity may select Los Angeles and pitching. A stored profile may then becreated containing the terms Dodgers, Los Angeles, and pitching. Inother embodiments, following the selection, additional terms may bedisplayed by the user device 130 that are determined by thedisambiguation engine to be relevant on the basis of all terms enteredthusfar.

Examples of scoring content based on user preferences and identifyingrelated relevant keywords have been described above in both an exampleof disambiguating preference information and scoring content forselection. The calculation of a relevance number based on preferenceterms was described.

From the foregoing it will be appreciated that, although specificembodiments of the invention have been described herein for purposes ofillustration, various modifications may be made without deviating fromthe spirit and scope of the invention.

1-30. (canceled)
 31. A method for electronic profile management by aprofiling system configured to serve as a trusted intermediary betweenan entity and a provider, the method comprising: storing a plurality ofelectronic profiles in an electronic storage medium, each electronicprofile from the plurality of electronic profiles associated with arespective entity from a plurality of entities, and each electronicprofile from the plurality of electronic profiles including at least onepreference of the respective entity and permissions configurable by therespective entity for access to the electronic profile; receiving, at aprofiling system in communication with the electronic storage medium, arequest from an electronic device associated with a provider to evaluatea plurality of content items of the provider for a first entity, whereinthe provider is different from the first entity; if permissionsassociated with an electronic profile of the first entity are configuredto allow access to the electronic profile of the first entity forevaluating content items of the provider, accessing, by the profilingsystem, the electronic profile associated with the first entity, theelectronic profile associated with the first entity being inaccessibledirectly by the provider; and computing a degree of relevance of eachcontent item from the plurality of content items to the electronicprofile of the first entity based in part on information retrieved fromthe electronic profile of the first entity including a preference of thefirst entity.
 32. The method according to claim 31 further comprising:transmitting to the electronic device associated with the provider atleast one of the computed degrees of relevance, an indication ofselected content items based on the computed degrees of relevance, orcombinations thereof.
 33. The method according to claim 1, whereinstoring a plurality of electronic profiles in an electronic storagemedium comprises storing electronic profiles in a relational database onthe electronic storage medium.
 34. The method according to claim 1,wherein the first entity comprises a person, a thing, a segment ofpeople, or a segment of things.
 35. The method according to claim 1,wherein computing a degree of relevance comprises generating an index byindexing the plurality of content items according to terms, and queryingthe index using a preference of the entity.
 36. The method according toclaim 35, wherein computing a degree of relevance further comprisescomputing a relevance number based on term frequency and inversedocument frequency calculations for each content item using thepreference of the first entity.
 37. The method according to claim 36,wherein the preference of the first entity comprises a plurality ofpreference terms, the relevance number computed in part by summing termfrequency and inverse document frequency calculations using eachpreference term from the plurality of preference terms.
 38. The methodaccording to claim 36, wherein the electronic profile of the firstentity comprises a plurality of preferences, and the relevance number iscomputed in part by summing respective relevance numbers calculatedusing each of the plurality of preferences.
 39. The method according toclaim 38, wherein summing respective relevance numbers comprises takinga weighted sum of the respective relevance numbers, and wherein a weightassociated with each preference from the plurality of preferences isdetermined in part based on a context of the first entity.
 40. Themethod according to claim 31 further comprising: receiving from thefirst entity a preference term of the first entity; and disambiguatingthe received preference term.
 41. The method according to claim 40,wherein disambiguating the received preference term comprisesidentifying relevant documents in a content store using the receivedpreference term and identifying a relevant keyword from at least one ofthe identified relevant documents, the method further comprising storingthe relevant keyword in the electronic storage medium as part of thepreference of the first entity.
 42. The method according to claim 41,wherein the content store is selected from a group of content storesbased in part on the preference.
 43. The method according to claim 41,wherein the act of computing the degree of relevance comprises utilizinga neural network to compute the degree of relevance.
 44. The methodaccording to claim 43, further comprising receiving feedback from anentity regarding the degree of relevance and modifying the neuralnetwork based on the feedback.
 45. A profiling system comprising: aprofile management system in communication with one or more electronicstorage media configured to store a plurality of electronic profiles,each electronic profile from the plurality of electronic profilesassociated with a respective entity from a plurality of entities, andeach electronic profile from the plurality of electronic profilesincluding at least one preference of the respective entity andpermissions configurable by the respective entity for access to theelectronic profile; and an analysis engine configured to receive arequest from a requestor to evaluate options of the requestor for afirst entity, the requestor being different from the first entity, theanalysis engine further configured to access an electronic profile ofthe first entity if permissions associated with the electronic profileof the first entity are configured to allow access to the electronicprofile of the first entity for evaluating options of the requestor, theelectronic profile of the first entity being inaccessible directly bythe requestor, the analysis engine further configured to compute arelevance number for each of the respective options based on theelectronic profile of the first entity including a preference of thefirst entity.
 46. The profiling system of claim 45, wherein the profilemanagement system is further configured to generate a profile for arespective entity based on profile information received by the profilemanagement system from the respective entity, the profile informationincluding the at least one preference of the respective entity.
 47. Theprofiling system of claim 45, wherein the plurality of electronicprofiles include entries in a relational database, each electronicprofile including a data table containing data associated with therespective entity and a related user preferences table containingpreference IDs for each preference associated with the respectiveentity.
 48. The profiling system of claim 46, wherein each electronicprofile further includes a user preferences terms table related to theuser preferences table, the user preferences terms table containing termIDs for each term associated with a preference.
 49. The profiling systemof claim 48, wherein each electronic profile further includes apreference terms table, the preference terms table related to the userpreference terms table, the preference terms table containing at leastone term associated with a respective term ID.
 50. The profiling systemof claim 45 further comprising: a disambiguation engine configured toreceive the preference of the first entity, identify terms related tothe preference of the first entity, and store at least one identifiedterm in an electronic storage medium in which the electronic profile ofthe first entity is stored.
 51. The profiling system of claim 50 furthercomprising: an indexing engine configured to index a plurality ofcontent items according to terms contained in the plurality of contentitems and store the indexed content items in an indexed content store;and wherein the disambiguation engine is coupled to the indexed contentstore and configured to identify terms related to the preference of thefirst entity by querying the indexed content store with the preferenceof the first entity.
 52. The profiling system of claim 51, wherein theindexing engine is configured to generate a plurality of indexed contentstores, each corresponding to a respective category, the disambiguationengine configured to select a category-specific content store of theplurality of content stores.
 53. The profiling system of claim 45,wherein the analysis engine is configured to compute the relevancenumber for each of the respective options based in part on calculating,for each option, term frequency and inverse document frequency numbersfor each term in the preference of the first entity.
 54. The profilingsystem of claim 45, wherein the analysis engine is further configured toprovide the requestor with at least a portion of the calculatedrelevance numbers, an indication of options associated with selectedrelevance numbers, or combinations thereof.